class_docstr: Callback that prints metrics to stdout.
  
  Arguments:
      count_mode: One of "steps" or "samples".
          Whether the progress bar should
          count samples seen or steps (batches) seen.
      stateful_metrics: Iterable of string names of metrics that
          should *not* be averaged over an epoch.
          Metrics in this list will be logged as-is.
          All others will be averaged over time (e.g. loss, etc).
          If not provided, defaults to the `Model`'s metrics.
  
  Raises:
      ValueError: In case of invalid `count_mode`.
code: def __init__(self, count_mode="samples", stateful_metrics=None):
      super(ProgbarLogger, self).__init__()
      if count_mode == "samples":
          self.use_steps = False
      elif count_mode == "steps":
          self.use_steps = True
      else:
          raise ValueError("Unknown `count_mode`: " + str(count_mode))
      # Defaults to all Model's metrics except for loss.
      self.stateful_metrics = (
          set(stateful_metrics) if stateful_metrics else None
      )
  
      self.seen = 0
      self.progbar = None
      self.target = None
      self.verbose = 1
      self.epochs = 1
  
      self._called_in_fit = False
  
imports: import copy
  import os
  import sys
  import time
  import numpy
  from .callback import Callback
label_desc: Use image node_id 1 to create a new ProgbarLogger object from inherited base classes: Callback with example: obj = ProgbarLogger(count_mode, stateful_metrics)